https://github.com/chanupadeshan/advanced-data-analytics

basic data analysis project using sql
https://github.com/chanupadeshan/advanced-data-analytics

cumulative-analysis performance-analysis sql

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basic data analysis project using sql

• Host: GitHub
• URL: https://github.com/chanupadeshan/advanced-data-analytics
• Owner: chanupadeshan
• Created: 2025-10-17T11:46:15.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2025-10-17T11:54:48.000Z (9 months ago)
• Last Synced: 2025-10-18T14:49:50.455Z (9 months ago)
• Topics: cumulative-analysis, performance-analysis, sql
• Language: TSQL
• Homepage:
• Size: 791 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# advanced-data-analytics

This repository contains SQL Server examples that build a small analytics warehouse and explore richer BI-style reporting. It expands on the basic project with customer, product, segmentation, and part-to-whole analyses. For the foundational setup, see Part 1, **Basic Data Analytics**, at https://github.com/chanupadeshan/basic-data-analytics.

## Contents

- `datasets/` — sample CSV files (`gold.dim_customers.csv`, `gold.dim_products.csv`, `gold.fact_sales.csv`)

- `script/` — SQL scripts that create and analyze the `DataWarehouseAnalytics` database:

  - `initialize_database.sql` — creates the database, `gold` schema, tables, and bulk-loads the CSV files (drops the database if it already exists).

  - `change_over_time.sql` — monthly sales trends covering totals, orders, averages, and items sold.

  - `cumulative_analysis.sql` — cumulative sales by month/year plus a yearly moving average.

  - `performance_analysis.sql` — product performance compared to the category average and the previous year.

  - `part_to_whole.sql` — breaks down sales contribution by product category and returns percentage-of-total metrics.

  - `Data_segmentation.sql` — segments products into cost ranges and buckets customers (VIP, Regular, New) based on lifetime value and tenure.

  - `customer_report.sql` — materializes `gold.report_customers` with KPIs such as total orders, sales, recency, lifespan, and spend segmentation.

  - `product_report.sql` — materializes `gold.report_products` with sales KPIs, product segmentation, pricing insights, and recency tracking.